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Testing Linearity in an AR Errors-in-variables Model with Application to Stochastic Volatility

D. Feldmann, W. Härdle, C. Hafner, M. Hoffmann, O. Lepski, A. Tsybakov (2003)

Applicationes Mathematicae

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric restrictions are justified for a given data set, a statistical test is required. In this paper, we develop such a test of a linear hypothesis versus a general composite nonparametric alternative using the state space representation of the SV model as an errors-in-variables AR(1) model. The power of the test is analyzed. We provide a simulation study and apply the test to the HFDF96...

Theory of classification : a survey of some recent advances

Stéphane Boucheron, Olivier Bousquet, Gábor Lugosi (2005)

ESAIM: Probability and Statistics

The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have led to these recent results.

Theory of Classification: a Survey of Some Recent Advances

Stéphane Boucheron, Olivier Bousquet, Gábor Lugosi (2010)

ESAIM: Probability and Statistics

The last few years have witnessed important new developments in the theory and practice of pattern classification. We intend to survey some of the main new ideas that have led to these recent results.

Trimmed Estimators in Regression Framework

TomĂĄĹĄ Jurczyk (2011)

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica

From the practical point of view the regression analysis and its Least Squares method is clearly one of the most used techniques of statistics. Unfortunately, if there is some problem present in the data (for example contamination), classical methods are not longer suitable. A lot of methods have been proposed to overcome these problematic situations. In this contribution we focus on special kind of methods based on trimming. There exist several approaches which use trimming off part of the observations,...

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